Evidence mapPaperPMID 40817342Full record

ArticleScientific reports2025

Modelling the spread of infectious diseases in public transport systems under varying demand patterns and capacity constraints.

László Hajdu, Jovan Pavlović, Miklós Krész, András Bóta

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

László HajduInnoRenew CoE, UP IAM and UP FAMNIT, University of Primorska, Titov trg 4, 6000, Koper, Slovenia.
Jovan PavlovićFAMNIT, University of Primorska, Glagoljaška 8, 6000, Koper, Slovenia.
Miklós KrészInnoRenew CoE, UP IAM and UP FAMNIT, University of Primorska, Titov trg 4, 6000, Koper, Slovenia.
András BótaEmbedded Intelligent Systems Lab, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187, Luleå, Sweden. andras.bota@ltu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the dynamics of passenger interactions and their epidemiological impact across public transportation systems is crucial for both service efficiency and public health. High passenger density and close physical proximity have been shown to accelerate the spread of infectious diseases. During the COVID-19 pandemic, many public transportation companies took measures to slow down and minimize the spread of the disease. One of these measures was introducing spacing and capacity constraints on public transit vehicles. Our objective is to explore the effects of changes in demand and transportation measures from an epidemiological point of view, offering alternative measures to public transportation companies to keep the system operational while minimizing the epidemiological risk as much as possible. Our findings show that restricting vehicle capacity can significantly reduce the spread of infections, while demand-related measures have an even stronger effect. Combining these approaches offers the best solutions for balancing public health and operability.

Indexed as

Communicable DiseasesCOVID-19TransportationHumansModels, TheoreticalPandemicsPublic HealthSARS-CoV-2Epidemic modellingNetwork algorithmsPublic transportation

Identifiers

PMID40817342
PMCPMC12356971

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.